AI-Driven Cyber Defense Systems Using Real-Time Analytics
TL;DR
The findings indicate that AI-powered cyber defense significantly enhances threat detection, reduces response time, and improves overall cyber resilience compared to traditional security models, highlighting its critical role in next-generation cybersecurity infrastructures.
Abstract
The rapid digitization of critical infrastructure, businesses, and government services has expanded the cyber-attack surface, making traditional security mechanisms increasingly ineffective. AI-based cyber defense systems powered by real-time analytics provide a proactive and adaptive approach to cybersecurity by integrating machine learning, deep learning, and intelligent threat detection techniques. This study examines the architecture, analytical frameworks, and operational processes of AI-driven cyber defense solutions capable of detecting known and unknown threats, including zero-day attacks and advanced persistent threats (APTs). The proposed framework incorporates continuous monitoring, streaming analytics, anomaly detection, behavioral analysis, and automated response mechanisms. Performance is evaluated using metrics such as detection accuracy, false positive rate, response time, and scalability. The findings indicate that AI-powered cyber defense significantly enhances threat detection, reduces response time, and improves overall cyber resilience compared to traditional security models, highlighting its critical role in next-generation cybersecurity infrastructures.